Finally, the capsule component is removed, and the CNN spatial features obtained from different LF SA images are directly concatenated for classification. It is worth noting that the SE-ResNet-50 [1]} and VGG-16 [1]} results presented in Tables REF  through REF  are applied to a single 2D central SA image, while the features extracted from all the views are concatenated here. Removing the capsule sub-network
decreases the recognition performance of the proposed CapsField solution as it limits the ability to learn the inter-view/angular relations between the spatial features. Moreover, in the absence of the capsule component, it is not possible to assign higher weights to the more relevant features. These observations reveal the added value of the capsule sub-network in learning a model that fully exploits the angular features available in LF images.
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